The present technique relates generally to imaging techniques and more particularly to feature identification within digital images. Specifically the technique relates to the use of computer implemented routines to assist in the reconciliation of two or more sets of classified features in an image data set.
Various technical fields engage in some form of image evaluation and analysis in which the identification and classification of recognizable features within the image data is a primary goal. For example, medical imaging technologies produce various types of diagnostic images which a doctor or radiologist may review for the presence of identifiable features of diagnostic significance. Similarly, in other fields, other features may be of interest. For example, non-invasive imaging of package and baggage contents may similarly be reviewed to identify and classify recognizable features. In addition, the analysis of satellite and radar weather data may involve the determination of what weather formations, such as tornados or other violent storms, are either present in the image data or are in the process of forming. Likewise, evaluation of astronomical and geological data represented visually may also involve similar feature identification exercises. With the development of digital imaging and image processing techniques, the quantity of readily available image data requiring analysis in many of these technical fields has increased substantially.
Indeed, the increased amounts of available image data may inundate the human resources, such as trained technicians, available to process the data. For example, it is often desirable to have a second trained technician independently process or “read” the data. This is a rather time-consuming and expensive practice, but one that is highly valued, particularly in medical diagnostics. However, in addition to the time taken to perform the second read of the data, time is also required to compare results and to resolve any discrepancies between the independent reads such that a final interpretation of the data may be determined. These discrepancies may occur at different levels, including discrepancies in detecting a feature, segmenting the feature from the surrounding image, classifying the feature, or in regard to other distinctions associated with the feature.
The readers may meet periodically to discuss and resolve discrepancies as well as to determine those cases on which they concur. These periodic meetings also allow the readers to hone their skills by discussing and evaluating the more difficult data which generally gives rise to discrepancies. To prepare and conduct these meetings, however, valuable time may be spent combining the data and flagging the discrepancies as well as the concurrences if those are to be reviewed as well. Likewise, the presentation of data to be discussed in such a meeting may be unnecessarily complicated by the inclusion of data for which there is no discrepancy, though this information may be of interest in other contexts. In addition, the efficiency of the process may be reduced in the absence of reader notes and assessments correlated with the discrepancies, which might facilitate a rapid assessment and reconciliation of many of the discrepancies.
In addition, groups of readers, such as in a class or educational setting, may independently read an image data set as part of the educational process. Feedback regarding performance in such an educational setting may be most productively focused on the discrepancies between independent reads and not on data where there is little, if any, disagreement. Likewise, panels of experts may also independently read an image data set in order to provide a consensus interpretation of the data, which may be used to train automated detection and classification routines such as those used in computer-assisted detection (CAD) algorithms. To the extent such expert panels are also evaluating difficult data, presumably the data most likely to cause problems for automated routines, a streamlined reconciliation process may also be beneficial.